Publications
Authors:
  • James Henderson , Diana Popa
Citation:
ACL, Berlin, Germany, August 7-12, 2016
Abstract:
Distributional semantics creates vectorspace
representations that capture many
forms of semantic similarity, but their relation
to semantic entailment has been less
clear. We propose a vector-space model
which provides a formal foundation for a
distributional semantics of entailment. Using
a mean-field approximation, we develop
approximate inference procedures
and entailment operators over vectors of
probabilities of features being known (versus
unknown). We use this framework
to reinterpret an existing distributionalsemantic
model (Word2Vec) as approximating
an entailment-based model of the
distributions of words in contexts, thereby
predicting lexical entailment relations. In
both unsupervised and semi-supervised
experiments on hyponymy detection, we
get substantial improvements over previous
results.
Year:
2016
Report number:
2016/033